- Thanks, it is an honor to be here
- First time presenting to Colombian audience
- Work in progress, comments welcome
- I'll talk about mergers with potential complements, let me start by defining…
Juan Vélez
February 04, 2017
Two ways one can think of complementarity
The complementarity creates pro-competitive effects
Bundling could create anti-competitive effects
Different than horizontal and vertical mergers
Twofold interest: dual character, regulatory agencies
Horizontal: produce substitutes (Staples and Office Depot, Bavaria and Águila)
Some complementarity is bound to happen
Anderson builds on Choi: when can a merger benefit consumers, harm outsiders and benefit insiders
A fringe of small ones (more than 70).
I use data from two sources
|
Means and standard deviations |
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| 2015 - Q1 | 2015 - Q2 | 2015 - Q3 | 2015 - Q4 | |
|---|---|---|---|---|
| Firm level | ||||
| # plans a | 22.831 | 24.12 | 21.965 | 22.631 |
| 21.124 | 22.167 | 19.765 | 18.149 | |
| # cities b | 24.312 | 24.111 | 27.304 | 27.125 |
| 20.651 | 21.31 | 22.651 | 24.435 | |
| Observations | 82 | 82 | 81 | 79 |
| Firm-market level | ||||
| Price c | 19.203 | 19.605 | 21.317 | 22.481 |
| 14.319 | 16.129 | 15.874 | 15.104 | |
| # plans d | 6.819 | 6.617 | 6.429 | 6.597 |
| 6.776 | 6.443 | 5.601 | 6.335 | |
| # subscribers | 32341.879 | 32346.814 | 32410.612 | 32496.101 |
| 31516.417 | 30478.309 | 32271.051 | 33064.021 | |
| Observations | 2341 | 2346 | 2347 | 2351 |
|
a: total plans offered across markets. b: number of cities where firm operates. c: price of bundle in 2015 dollars. The number of households subscribed is used as weights. d: number of plans offered in market. |
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|
Means and standard deviations |
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| 2015 - Q1 | 2015 - Q2 | 2015 - Q3 | 2015 - Q4 | |
|---|---|---|---|---|
| Price a | 26.388 | 29.718 | 28.31 | 27.55 |
| 21.127 | 28.271 | 21.877 | 22.101 | |
| Download speed b | 2.285 | 3.121 | 3.426 | 3.375 |
| 1.921 | 2.815 | 3.107 | 3.264 | |
| Premium channels c | 0.312 | 0.336 | 0.345 | 0.388 |
| 0.304 | 0.325 | 0.318 | 0.342 | |
| HD channels | 0.265 | 0.211 | 0.293 | 0.238 |
| 0.187 | 0.177 | 0.265 | 0.148 | |
| Gb Included (Mobile) d | 3.421 | 3.678 | 3.762 | 3.518 |
| 2.901 | 2.875 | 2.886 | 3.107 | |
| Minutes included (Cell phone) | 301.078 | 409.123 | 410.356 | 452.31 |
| 100.078 | 102.891 | 132.376 | 137.552 | |
| Gb included (Cell phone) e | 2.678 | 2.889 | 3.173 | 3.204 |
| 4.143 | 5.168 | 5.284 | 4.178 | |
| Minutes included (Phone) | 435.218 | 482.337 | 473.215 | 461.461 |
| 212.166 | 283.472 | 251.639 | 258.813 | |
| Observations | 28934 | 28965 | 28919 | 28727 |
|
a: Price in 2015 dollars. b: of bundles with Internet. In megabits per second. c: channels like HBO. For bundles with TV. d: mobile refers to mobile internet provided through a dongle. e: access to internet using a cell phone as opposed to using a USB modem and a computer. |
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Following Gentzkow (2007)
ℱ firms indexed by
indexes the standalone goods provided by a firm
indexes bundles provided by a firm
are types of bundles
Example: Sutatausa. ℱ = 4 (Claro, UNE, Coltel and ETB). Claro provides TV, Mobile Internet and Cellphone. Bundles: TV/Mobile (1.2Gb), TV/Mobile (2Gb), Cellphone/TV).
Suppose , and are observed
Suppose
That the goods are Internet (A) and TV (B)
Large relative to and
Internet (A) and TV (B) and true Gamma >0. increase in download speeds should also affect S_B
If gamma = 0 shouldn't
Or think of 2 markets where speed varies. If the market with higher speed has also larger shares for TV and bundle, the model has to rationalize that with a large Gamma
Unobserved characteristics may be correlated with price
Instruments
BLP instruments
Hausman instruments
Firms are assumed to play a static Bertrand game
|
Bundle characteristics |
||||||||
| OLS |
BLP Instruments |
Hausman Instruments |
Random Coef. | |||||
|---|---|---|---|---|---|---|---|---|
| Price | -0.163 | ** | -0.381 | ** | -0.411 | ** | -0.479 | ** |
| 0.081 | 0.154 | 0.178 | 0.224 | |||||
| Download speed | 2.235 | ** | 1.571 | ** | 1.792 | ** | 2.012 | * |
| 1.047 | 0.648 | 0.841 | 1.088 | |||||
| Premium channels | -0.065 | 0.031 | 0.028 | 0.091 | * | |||
| 0.067 | 0.045 | 0.029 | 0.046 | |||||
| HD channels | 0.078 | * | 1.318 | ** | 1.285 | ** | 1.247 | * |
| 0.069 | 0.661 | 0.517 | 0.661 | |||||
| Gb included (Mobile) | 1.046 | ** | 1.048 | ** | 1.035 | ** | 1.037 | ** |
| 0.482 | 0.511 | 0.456 | 0.496 | |||||
| Minutes included (Cell) | 0.045 | *** | 0.029 | ** | 0.035 | ** | 0.027 | ** |
| 0.012 | 0.013 | 0.012 | 0.012 | |||||
| Gb included (Cell) | 0.468 | *** | 0.402 | ** | 0.395 | ** | 0.673 | ** |
| 0.114 | 0.2 | 0.197 | 0.285 | |||||
| Minutes included (Landline) | -0.035 | 0.018 | 0.018 | 0.017 | ||||
| 0.231 | 0.086 | 0.077 | 0.45 | |||||
| Constant | -12.489 | *** | -12.952 | *** | -13.457 | *** | -14.357 | *** |
| 0.315 | 1.47 | 1.057 | 2.714 | |||||
|
Continuous variables in logs. Number of osbervations: 115,545 Signif. codes: 0.001(***) 0.05(**) 0.10(*). |
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|
|
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| Γ | Dollars | |||
|---|---|---|---|---|
| Internet/Phone | 0.82 | *** | $0.92 | |
| 0.13 | ||||
| Internet/TV | -1.78 | * | -$2.00 | |
| 1.6 | ||||
| Internet/Mobile | 1.35 | *** | $1.51 | |
| 0.27 | ||||
| Internet/Cell | 2.79 | ** | $3.13 | |
| 1.38 | ||||
| Phone/TV | 0.92 | *** | $1.03 | |
| 0.04 | ||||
| Phone/Mobile | -2.11 | *** | -$2.36 | |
| 0.57 | ||||
| Phone/Cell | 1.02 | *** | $1.15 | |
| 0.02 | ||||
| TV/Mobile | 0.02 | $0.02 | ||
| 0.01 | ||||
| TV/Cell | 1.73 | *** | $1.94 | |
| 0.01 | ||||
| Mobile/Cell | 2.57 | * | $2.89 | |
| 1.22 | ||||
| Internet/Phone/TV | 1.17 | *** | $1.31 | |
| 0.26 | ||||
| The estimation algorithm is initialized with simple Logit estimates. *** significant at 1%;** significant at 5% | ||||
All signigifacnt except for TV-Mobile
|
|
||||
| Γ | Dollars | |||
|---|---|---|---|---|
| Internet/Phone | 0.82 | *** | $0.92 | |
| 0.13 | ||||
| Internet/TV | -1.78 | * | -$2.00 | |
| 1.6 | ||||
| Internet/Mobile | 1.35 | *** | $1.51 | |
| 0.27 | ||||
| Internet/Cell | 2.79 | ** | $3.13 | |
| 1.38 | ||||
| Phone/TV | 0.92 | *** | $1.03 | |
| 0.04 | ||||
| Phone/Mobile | -2.11 | *** | -$2.36 | |
| 0.57 | ||||
| Phone/Cell | 1.02 | *** | $1.15 | |
| 0.02 | ||||
| TV/Mobile | 0.02 | $0.02 | ||
| 0.01 | ||||
| TV/Cell | 1.73 | *** | $1.94 | |
| 0.01 | ||||
| Mobile/Cell | 2.57 | * | $2.89 | |
| 1.22 | ||||
| Internet/Phone/TV | 1.17 | *** | $1.31 | |
| 0.26 | ||||
| The estimation algorithm is initialized with simple Logit estimates. *** significant at 1%;** significant at 5% | ||||
With estimates for the preferences, we are ready to simulate 2 scenarios:
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|
1st Quartile |
2nd Quartile |
3rd Quartile |
Mean | Min. | Max. | |
|---|---|---|---|---|---|---|
| Baselinea | $7.79 | $10.40 | $16.01 | $13.68 | $1.03 | $49.28 |
| Mergerb | $8.23 | $10.72 | $16.31 | $13.72 | $0.95 | $49.21 |
| (a) Original market structure; (b) ETB and Avantel. | ||||||
|
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|
1st Quartile |
2nd Quartile |
3rd Quartile |
Mean | Min. | Max. | |
|---|---|---|---|---|---|---|
| Baselinea | $14.16 | $17.02 | $25.88 | $20.28 | $8.31 | $153.83 |
| Mergerb | $13.33 | $14.59 | $22.55 | $18.49 | $7.15 | $153.97 |
| (a) Original market structure; (b) ETB and Avantel. | ||||||
|
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|
1st Quartile |
2nd Quartile |
3rd Quartile |
Mean | Min. | Max. | |
|---|---|---|---|---|---|---|
| Baselinea | $7.23 | $9.72 | $15.30 | $12.90 | $0.05 | $49.12 |
| Mergerb | $6.79 | $9.40 | $15.01 | $12.86 | $0.03 | $49.13 |
| (a) Original market structure; (b) ETB and Avantel. | ||||||
|
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|
1st Quartile |
2nd Quartile |
3rd Quartile |
Mean | Min. | Max. | |
|---|---|---|---|---|---|---|
| Baselinea | $18.48 | $28.60 | $37.62 | $28.57 | $5.99 | $158.18 |
| Mergerb | $20.72 | $28.96 | $37.45 | $29.42 | $6.15 | $158.22 |
| (a) Original market structure; (b) ETB and Avantel. | ||||||
As expected there is a slight reduction in standalone prices but the price of bundles increases
Consumer variation is a negative 11 million
Extensions
Policy implications
Future work
|
Means and standard deviations |
||||||
| Stratum 1 | Stratum 2 | Stratum 3 | Stratum 4 | Stratum 5 | Stratum 6 | |
|---|---|---|---|---|---|---|
| Avg. Schooling a | 6.94 | 8.28 | 9.72 | 11.75 | 11.85 | 13.86 |
| 2.45 | 2.49 | 2.96 | 3.42 | 3.27 | 3.42 | |
| HH age b | 0.68 | 0.61 | 0.62 | 0.59 | 0.56 | 0.65 |
| 0.47 | 0.49 | 0.49 | 0.49 | 0.5 | 0.48 | |
| Family size | 5.34 | 4.76 | 3.65 | 3.86 | 3.59 | 3.37 |
| 3.45 | 2.27 | 1.48 | 1.62 | 1.53 | 1.52 | |
| Monthly income c | 386.26 | 534.17 | 556.37 | 933.36 | 1084.72 | 1957.3 |
| 366.42 | 363.72 | 523.72 | 997.25 | 1549.46 | 2277.01 | |
| Observations | 720 | 720 | 720 | 720 | 390 | 390 |
|
a: sum of the number of years of schooling for people in the household, devided by the number of members of the household. b: head of the household is between 25 and 45 years old. c: Monthly income in 2015 dollars |
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Let . Then, the choice probabilities are:
Let and .
Definition: A and B are complements if , susbtitutes if and independent if .
Anderson, Simon P., Simon Loertscher, and Yves Schneider. 2010. “The ABC of Complementary Products Mergers.” Economics Letters 106 (3): 212–15. doi:10.1016/j.econlet.2009.11.022.
Choi, Jay Pil. 2008. “Mergers with Bundling in Complementary Markets.” The Journal of Industrial Economics 56 (3). wiley: 553–77. doi:10.1111/j.1467-6451.2008.00352.x.
Flores-Fillol, Ricardo, and Rafael Moner-Colonques. 2011. “Endogenous Mergers of Complements with Mixed Bundling.” Review of Industrial Organization 39 (3): 231–51. doi:10.1007/s11151-011-9281-0.
Gentzkow, Matthew. 2007. “Valuing New Goods in a Model with Complementarity: Online Newspapers.” American Economic Review 97 (3): 713–44. doi:10.1257/aer.97.3.713.
Peitz, Martin. 2008. “Bundling May Blockade Entry.” International Journal of Industrial Organization 26 (1): 41–58. doi:10.1016/j.ijindorg.2006.09.005.
Ribeiro, Ricardo, and Joao Vareda. 2010. “Crowding Out or Complementarity in the Telecommunications Market.” Economics Letters 106 (3). Elsevier: 212–15.
Spector, David. 2007. “Bundling, Tying, and Collusion.” International Journal of Industrial Organization 25 (3): 575–81. doi:10.1016/j.ijindorg.2006.06.003.